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{
"cells": [
{
"cell_type": "code",
"execution_count": 94,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "cf86fed9cae54700b31a616cd82b7180",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Generating train split: 0 examples [00:00, ? examples/s]"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from datasets import load_dataset\n",
"# load dataset from data.jsonl file:\n",
"eli5 = load_dataset(\"json\", data_files=\"data3.jsonl\", split=\"train[:80%]\")"
]
},
{
"cell_type": "code",
"execution_count": 95,
"metadata": {},
"outputs": [],
"source": [
"eli5 = eli5.train_test_split(test_size=0.2)"
]
},
{
"cell_type": "code",
"execution_count": 96,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'text': \"Extract the calendar events from the following text, the text will contain a place, time , land possibly a location. Here is the text: : Board meeting next Tuesday at 10 AM.\\nThe Details are as follows: {'datetime': '2024-03-19T10:00:00', 'description': 'Board meeting', 'location': ''}\\n\"}"
]
},
"execution_count": 96,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"eli5[\"train\"][0]"
]
},
{
"cell_type": "code",
"execution_count": 97,
"metadata": {},
"outputs": [],
"source": [
"from transformers import AutoTokenizer\n",
"\n",
"tokenizer = AutoTokenizer.from_pretrained(\"distilbert/distilgpt2\")"
]
},
{
"cell_type": "code",
"execution_count": 98,
"metadata": {},
"outputs": [],
"source": [
"eli5 = eli5.flatten()"
]
},
{
"cell_type": "code",
"execution_count": 99,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'text': \"Extract the calendar events from the following text, the text will contain a place, time , land possibly a location. Here is the text: : Board meeting next Tuesday at 10 AM.\\nThe Details are as follows: {'datetime': '2024-03-19T10:00:00', 'description': 'Board meeting', 'location': ''}\\n\"}"
]
},
"execution_count": 99,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"eli5[\"train\"][0]"
]
},
{
"cell_type": "code",
"execution_count": 100,
"metadata": {},
"outputs": [],
"source": [
"def preprocess_function(examples):\n",
" return tokenizer([\" \".join(x) for x in examples])"
]
},
{
"cell_type": "code",
"execution_count": 101,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "7d326a1d4117454f98bfd6c7f575120c",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Map (num_proc=4): 0%| | 0/49 [00:00<?, ? examples/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "13f25ef46a43486ea69fec77f62f7c9a",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Map (num_proc=4): 0%| | 0/13 [00:00<?, ? examples/s]"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"tokenized_eli5 = eli5.map(\n",
" preprocess_function,\n",
" batched=True,\n",
" num_proc=4,\n",
" remove_columns=eli5[\"train\"].column_names,\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 102,
"metadata": {},
"outputs": [],
"source": [
"block_size = 128\n",
"\n",
"\n",
"def group_texts(examples):\n",
" # Concatenate all texts.\n",
" concatenated_examples = {k: sum(examples[k], []) for k in examples.keys()}\n",
" total_length = len(concatenated_examples[list(examples.keys())[0]])\n",
" # We drop the small remainder, we could add padding if the model supported it instead of this drop, you can\n",
" # customize this part to your needs.\n",
" if total_length >= block_size:\n",
" total_length = (total_length // block_size) * block_size\n",
" # Split by chunks of block_size.\n",
" result = {\n",
" k: [t[i : i + block_size] for i in range(0, total_length, block_size)]\n",
" for k, t in concatenated_examples.items()\n",
" }\n",
" result[\"labels\"] = result[\"input_ids\"].copy()\n",
" return result"
]
},
{
"cell_type": "code",
"execution_count": 103,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "a571ed26269640278514bfb2b02b1e03",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Map (num_proc=4): 0%| | 0/4 [00:00<?, ? examples/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "3385544255ec4af79d74e2d131845e07",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Map (num_proc=4): 0%| | 0/4 [00:00<?, ? examples/s]"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"lm_dataset = tokenized_eli5.map(group_texts, batched=True, num_proc=4)"
]
},
{
"cell_type": "code",
"execution_count": 104,
"metadata": {},
"outputs": [],
"source": [
"from transformers import DataCollatorForLanguageModeling\n",
"\n",
"tokenizer.pad_token = tokenizer.eos_token\n",
"data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=False)"
]
},
{
"cell_type": "code",
"execution_count": 105,
"metadata": {},
"outputs": [],
"source": [
"from transformers import AutoModelForCausalLM, TrainingArguments, Trainer\n",
"\n",
"model = AutoModelForCausalLM.from_pretrained(\"distilbert/distilgpt2\")"
]
},
{
"cell_type": "code",
"execution_count": 106,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Model moved to MPS device\n"
]
}
],
"source": [
"import torch\n",
"\n",
"# Check that MPS is available\n",
"if not torch.backends.mps.is_available():\n",
" if not torch.backends.mps.is_built():\n",
" print(\"MPS not available because the current PyTorch install was not \"\n",
" \"built with MPS enabled.\")\n",
" else:\n",
" print(\"MPS not available because the current MacOS version is not 12.3+ \"\n",
" \"and/or you do not have an MPS-enabled device on this machine.\")\n",
"\n",
"else:\n",
" mps_device = torch.device(\"mps\")\n",
" model.to(mps_device)\n",
" print(\"Model moved to MPS device\")"
]
},
{
"cell_type": "code",
"execution_count": 107,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "a71332654a414bfe87d416ce502c9cdc",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
" 0%| | 0/3 [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "eab9e9bcc2814d3e8ba55806dc9d4a4f",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
" 0%| | 0/1 [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'eval_loss': 6.667893886566162, 'eval_runtime': 0.0262, 'eval_samples_per_second': 152.548, 'eval_steps_per_second': 38.137, 'epoch': 1.0}\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "b9634b37ea2d436c9700ce311651fdae",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
" 0%| | 0/1 [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'eval_loss': 6.2145514488220215, 'eval_runtime': 0.1232, 'eval_samples_per_second': 32.47, 'eval_steps_per_second': 8.118, 'epoch': 2.0}\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "3af25ad29ad04c319677ec04dc22d3d1",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
" 0%| | 0/1 [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'eval_loss': 5.993268966674805, 'eval_runtime': 0.0204, 'eval_samples_per_second': 196.346, 'eval_steps_per_second': 49.087, 'epoch': 3.0}\n",
"{'train_runtime': 1.588, 'train_samples_per_second': 7.556, 'train_steps_per_second': 1.889, 'train_loss': 6.412024815877278, 'epoch': 3.0}\n"
]
},
{
"data": {
"text/plain": [
"TrainOutput(global_step=3, training_loss=6.412024815877278, metrics={'train_runtime': 1.588, 'train_samples_per_second': 7.556, 'train_steps_per_second': 1.889, 'train_loss': 6.412024815877278, 'epoch': 3.0})"
]
},
"execution_count": 107,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"training_args = TrainingArguments(\n",
" output_dir=\"my_awesome_eli5_clm-model\",\n",
" evaluation_strategy=\"epoch\",\n",
" learning_rate=2e-5,\n",
" weight_decay=0.01,\n",
" push_to_hub=True,\n",
")\n",
"\n",
"trainer = Trainer(\n",
" model=model,\n",
" args=training_args,\n",
" train_dataset=lm_dataset[\"train\"],\n",
" eval_dataset=lm_dataset[\"test\"],\n",
" data_collator=data_collator,\n",
")\n",
"\n",
"trainer.train()"
]
},
{
"cell_type": "code",
"execution_count": 110,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n"
]
},
{
"data": {
"text/plain": [
"[{'generated_text': \"Extract the calendar events from the following text, the text will contain a place, time , land possibly a location. Here is the text: : Let's meet for lunch tomorrow at 12 PM at the Italian restaurant on Main Street.\\nThe Details are as follows: { if (is_empty(the_time()) : return next_day_long(_.length(this); } } }\\nThe Time is: 12 PM on Sunday 12th at the Italian restaurant on Main Street.\\nTaste: 12 PM on Sunday 8th at the Italian restaurant on Main Street.\\nThe Time is: 11 PM on Monday 9th at the Italian restaurant on Main Street.\\nThe Time is: 11 AM on Monday 9th at the Italian restaurant on Main Street.\\nTaste: 11 AM on Sunday 8th at the Italian restaurant on Main Street.\\nThe Time is: 11 AM on Monday 9th at the Italian restaurant on Main Street.\\nThe Time is: 11 AM\"}]"
]
},
"execution_count": 110,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"prompt = \"Extract the calendar events from the following text, the text will contain a place, time , land possibly a location. Here is the text: : Let's meet for lunch tomorrow at 12 PM at the Italian restaurant on Main Street.\\nThe Details are as follows: {\"\n",
"from transformers import pipeline\n",
"\n",
"generator = pipeline(\"text-generation\", model=model, tokenizer=tokenizer, max_length=200)\n",
"generator(prompt)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from transformers import AutoTokenizer"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.12.1"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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